A Novel Heuristic Optimization Methodology for Solving of Economic Dispatch Problems

Authors

Abstract

This paper presents a biogeography-based optimization (BBO) algorithm to solve the economic load
Dispatch (ELD) problem with generator constraints in thermal plants. The applied method can solve
the ELD problem with constraints like transmission losses, ramp rate limits, and prohibited operating
zones. Biogeography is the science of the geographical distribution of biological species. The models
of biogeography explain how a organisms arises, immigrate from an environment to another and gets
eliminated. The BBO has some characteristics that are shared with other population based
optimization procedures, similar to genetic algorithms (GAs) and particle swarm optimization (PSO).
The BBO algorithm mainly based on two steps: migration and mutation. The BBO has some good
features in reaching to the global minimum in comparison to other evolutionary algorithms. This
algorithm applied on two practical test systems that have six and fifteen thermal units, results of this
paper are used to see the comparison between performances of the BBO algorithm with other existing
algorithms. The result of this investigation proves the efficiency and good performance of applying
BBO algorithm on ELD problem and show that this method can be a good substitute for other
algorithms.

Keywords


[1] A. J. Wood and B. F. Wollenberg, Power
Generation, Operation, and Control, 2nd ed.
New York: Wiley, 1996.
[2] A. A. El-Keib, H. Ma, and J. L. Hart,
“Environmentally constrained economic
dispatch using The Lagrangian relaxation
method,” IEEE Trans. Power Syst., vol. 9, no.
4, pp. 1723–1729, Nov. 1994.
[3] C.-T. Su and C.-T. Lin, “New approach with a
Hopfield modeling framework to economic
Dispatch,” IEEE Trans. Power Syst., vol.
15,no. 2, p. 541, May 2000.
[4] C.-T. Su and C.-T. Lin, “New approach with a
Hopfield modeling framework to economic
Dispatch,” IEEE Trans. Power Syst., vol. 15,
no. 2, p. 541, May 2000.
[5] P. H. Chen and H. C. Chang, “Large-scale
economic dispatch by genetic algorithm,” IEEE
Trans. Power Syst., vol. 10, no. 4, pp. 1919–

1926, Nov. 1995.

[6] Lin WM, Chen FS, Tsay MT. An improved
tabu search for economic dispatch with
multiple Minima. IEEE Trans Pow Syst
2002;17(1):108–12.
[7] Panigrahi B.K., Yadav S. R., Agrawal S. &
Tiwari M.K. (2007). A clonal algorithm to
solve Economic loadispatch. Electrical Power
System Research, 77: 1381-1389.
[8] K. P. Wong and C. C. Fung, “Simulated
annealing based economic dispatch
algorithm,” Proc. Inst. Elect. Eng. C, vol. 140,
no. 6, pp. 509–515, 1993.
[9] H. T. Yang, P. C. Yang, and C. L. Huang,
“Evolutionary programming based economic
dispatch for units with non-smooth fuel cost
functions,” IEEE Trans. Power Syst., vol. 11,
no. 1, pp. 112–118, Feb. 1996.
[10] D. Simon, “Biogeography-based optimization,”
IEEE Trans. Evol. Comput., vol. 12, no. 6, pp.
702–713, Dec. 2008.
[11] A. Bhattacharya and P. K. Chattopadhyay
“Biogeography-Based Optimization for
Different Economic Load Dispatch Problems”
IEEE Trans. Power Syst., VOL. 25, NO. 2 , pp
1064 - 1077 , MAY 2010.
[12] Z.-L. Gaing, “Particle swarm optimization to
solving the economic dispatch considering the
generator constraints,” IEEE Trans. Power
Syst., vol. 18, no. 3, pp. 1187–1195, Aug. 2003.
[13] I. Selvakumar and K. Thanushkodi, “A new
particle swarm optimization solution to
nonconvex economic dispatch problems,”
IEEE Trans. Power Syst., vol. 22, no. 1, pp.
42–51, Feb. 2007.
[14] K. T. Chaturvedi, M. Pandit, and L. Srivastava,
“Self-organizing hierarchical particle swarm
Optimization for nonconvex economic
dispatch,” IEEE Trans. Power Syst., vol. 23,
no. 3, p. 1079, Aug. 2008.
[15] Pereira-Neto A, Unsihuay C, Saavedra OR.
Efficient evolutionary strategy optimization
Procedure to solve the non convex economic
dispatch problem with generator constraints.
IEE Proc – GenerTransm Distrib 2005;
152(5):653–660.